Ultra-low Power Sensor Platform
Every year, students grow, learn and contribute to new findings, as they carry out their master thesis projects at QRTECH. For spring 2022 we have defined a project scope where you can be part of creating a sustainable IoT-solution for predictive maintenance. The project includes developing a hardware sensor platform, supervision algorithms or a secured cloud solution.
Dig deeper into what’s interesting you the most and contribute to new findings!
Background
When certain types of machinery run over long periods of time, partly unsupervised, an overheating event can lead to incineration where the machine typically is destroyed beyond repair. If the event is discovered in time, the machine can be stopped and the attempts to extinguish the fire can be initialized in time.
Within this project, the main target is to develop an ultra-low power sensor platform, able to discover of such critical events in time. The solution can also be possible to use for supervision purpose with respect to predictive maintenance.
This project includes hardware, supervision algorithms and a cloud solution. Although these parts are intended to work together, they can also be used in isolation.
Master Thesis
Supervision algorithms
The focus of this thesis is on developing an algorithm for general sensor supervision, allowing for both the detection of critical events as well as the supervision of machinery performance in order to allow for predictive maintenance.
Alternatively, two different algorithms can be developed, one for detecting critical events and one for predictive maintenance.
The algorithm should have a generalized structure in order to allow it to be used for any kind of sensor data (both in terms of data range and number of sensors). As it might be necessary to run the algorithm on a low power platform in order to minimize the time it takes to raise the alarm, the algorithm should be able to run on an embedded processor.
Master Thesis
Secured cloud solution
IoT security is the technology segment focused on safeguarding connected devices and networks in the internet of things (IoT).
Risk must be mitigated for the entire IoT lifecycle of the deployment, from web and mobile applications, cloud, and communications, to gateways, IoT sensors, supply chains, and data storage.
For this thesis you will investigate and define threat models and attack surfaces related to the IoT lifecycle, and apply suggested mitigations on an existing reference IoT cloud solution.
Master Thesis
Hardware sensor platform
The focus of this thesis is on developing the hardware for the sensor part. As the sensors might be embedded inside of the cover of the machine, the sensor platform needs an unusually long lifetime. The possibility of supplying power through different types of energy harvesting techniques, depending on the use case, is also important. Furthermore, communication with the central MCU needs to be over a wireless communication interface in order to avoid signalling cables, that can both be difficult to install as well as susceptible to wear.
It should be possible to build the sensor units with different types of sensors as long as these sensors fulfil the basic requirements. Example of sensors that might be of interest would be temperature, voltage or current sensors.
